Comparative Analysis of Deep Learning Methods on CT Images for Lung Cancer Specification
The study demonstrates the effectiveness of deep learning models, particularly InceptionResNetV2 and UNet, in both detecting and segmenting lung cancer, showing significant potential for aiding early diagnosis and treatment. Future work could focus on refining these models and exploring their application in other medical domains.
Authors
- Fatih Ekinci (ORCID: https://orcid.org/0000-0002-1011-1105)
- Mehmet Serdar Güzel (ORCID: https://orcid.org/0000-0002-3408-0083)
- Tunç Aşuroğlu (ORCID: https://orcid.org/0000-0003-4153-0764)
- Mürüvvet Kalkan (ORCID: https://orcid.org/0000-0001-8056-1905)
- Ebru Akçapınar Sezer (ORCID: https://orcid.org/0000-0002-9287-2679)
Institutions
- Ankara University (TR)
- Tampere University (FI)
- Hacettepe University (TR)
- VTT Technical Research Centre of Finland (FI)
Publication Details
- Journal
- Cancers
- Published
- 2024-09-28
- DOI
- https://doi.org/10.3390/cancers16193321
- Citations
- 16
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.47
Funders
- Türkiye Bilimsel ve Teknolojik Araştırma Kurumu